{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Different Activation Functions in Tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Defining a simple data set\n",
    "T, F = 1., -1.\n",
    "X_in = [\n",
    "    [T, T, T],\n",
    "    [T, T, F],\n",
    "    [T, F, T],\n",
    "    [T, F, F],\n",
    "    [F, T, T],\n",
    "    [F, T, F],\n",
    "    [F, F, T],\n",
    "    [F, F, F],\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Threshold Activation function\n",
    "def threshold (x):\n",
    "    cond = tf.less(x, tf.zeros(tf.shape(x), dtype = x.dtype))\n",
    "    out = tf.where(cond, tf.zeros(tf.shape(x)), tf.ones(tf.shape(x)))\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.]\n",
      " [ 1.]\n",
      " [ 0.]\n",
      " [ 1.]\n",
      " [ 0.]\n",
      " [ 1.]\n",
      " [ 0.]\n",
      " [ 0.]]\n"
     ]
    }
   ],
   "source": [
    "# Using Threshold Activation to calculate output for the given data\n",
    "\n",
    "b = tf.Variable(tf.random_normal([1,1], stddev=2))\n",
    "w = tf.Variable(tf.random_normal([3,1], stddev=2))\n",
    "h = tf.matmul(X_in, w) + b\n",
    "threshold_neuron = threshold(h)\n",
    "init = tf.global_variables_initializer()\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    out = sess.run(threshold_neuron)\n",
    "\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15cb4305b00>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TbHjOZoHtFgJrctNrgT071nkmMEfSRcDGwOcj4szOHUk6GjgaYNGiRQUObTb1slHXZiFp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15cb42b4d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Threshold Activation Function\n",
    "h = np.linspace(-1,1,50)\n",
    "out = threshold(h)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    y = sess.run(out)\n",
    "    \n",
    "plt.xlabel('Activity of Neuron')\n",
    "plt.ylabel('Output of Neuron')\n",
    "plt.title('Threshold Activation Function')\n",
    "plt.plot(h, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15cb42c7cc0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NiIgNwD8BZ+U6MLN8mfr8MsrLxDkTh+c7FLMu115SiIiIViY2sONuJLOS0tAY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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15e3a4bf668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Sigmoidal Activation function\n",
    "h = np.linspace(-10,10,50)\n",
    "out = tf.sigmoid(h)\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    y = sess.run(out)\n",
    "    \n",
    "plt.xlabel('Activity of Neuron')\n",
    "plt.ylabel('Output of Neuron')\n",
    "plt.title('Sigmoidal Activation Function')\n",
    "plt.plot(h, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15cb42d0cc0>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GxMKOdSLiS8CX0vXfCnwsIjZldnN6RGzow7DN+pUt23fxp2WNfPC0GYwc4q75rLzKeYVy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\nzIrCCcXMzIri/wGEHAYbD7La5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15e3a8d7898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Hyperbolic Tangent Activation function\n",
    "h = np.linspace(-10,10,50)\n",
    "out = tf.tanh(h)\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    y = sess.run(out)\n",
    "    \n",
    "plt.xlabel('Activity of Neuron')\n",
    "plt.ylabel('Output of Neuron')\n",
    "plt.title('Hyperbolic Tangent Activation Function')\n",
    "plt.plot(h, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3.93053699]\n",
      " [ 6.98585987]\n",
      " [-3.63593173]\n",
      " [-0.58060908]\n",
      " [ 0.05995795]\n",
      " [ 3.11528039]\n",
      " [-7.50651073]\n",
      " [-4.45118809]]\n"
     ]
    }
   ],
   "source": [
    "# Linear Activation Function\n",
    "b = tf.Variable(tf.random_normal([1,1], stddev=2))\n",
    "w = tf.Variable(tf.random_normal([3,1], stddev=2))\n",
    "linear_out = tf.matmul(X_in, w) + b\n",
    "init = tf.global_variables_initializer()\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    out = sess.run(linear_out)\n",
    "\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15e3e0ed780>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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utkRgZvWAMcDV4bWQd+PuT7l7H3fv07SpzoQQAVixYRtXjppIx2b1uFvF5KSc\nxJIIzKwGQRLIcPexccQgUtnszM7l8oxMduzK4YmhvalTUz8DkvKR8v8kC77C/AuY6e4PpHr7IpXV\nX96ZSebi9Tx2Ti/2a1ov7nCkComjRXAYcB5wjJlNCm8nxRCHSKXxxuTlPPPFQi44rD0nd2sRdzhS\nxaS8ReDunwPq2BSJaN7qzYwYM4VebRty04kHxh2OVEH6ZbFIBbZlR1BMrlaNaiomJ0mj0SaRCsrd\n+cOrU5mXtZnnL+xHiwYqJifJoa8XIhXUyK8X8fqk5Vz3804c3rFJ3OFIFaZEIFIBTVqynjvfnMEx\nBzTjsqP2jzscqeKUCEQqmHVbdnJ5RibN96rNA2d1VzE5STqNEYhUILm5ztWjJ5G1aQevDD+UhnVU\nTE6STy0CkQrkkY/n8emcLG4f1IVurVVMTlJDiUCkgvhsThYPfjSHM3q24py+beMOR9KIEoFIBbB8\n/TauGjWRTs3q8+fTVUxOUkuJQCRmO7NzuSwjk105zhNDe7FnzWpxhyRpRoPFIjG7++2ZTFqynsfP\n7cW+KiYnMVCLQCRG4yYv59kvF3LR4R046WAVk5N4KBGIxGTuqk2MGDOFPu0aMeLEA+IOR9KYEoFI\nDLbsyGZ4RiZ1albj0XN6UaOaPooSH40RiKSYuzNi7FTmZ21m5EX92KdB7bhDkjSnryEiKfbvrxbx\nxuTlXPeLzgzYX8XkJH5KBCIplLl4HXe9NYNjD2jG8J/tF3c4IoASgUjK/LBlJ1dkZLJPg9o8cFYP\nFZOTCkNjBCIpkJPrXDVqImu27GTs8AE0qFMj7pBE/kctApEUePijufx37hr+OKgrB7VqEHc4IrtR\nIhBJsk9mr+bhj+fyy16tGXxIm7jDEfkJJQKRJFq2fhvXjJ5E5+b1ueu0g1RMTiokJQKRJNmRncNl\nGZlk5zhPDO2tYnJSYWmwWCRJ7npzJpOXrOfJob3o0KRu3OGIFEotApEkeH3SMp7/ehEXH9GBEw5S\nMTmp2JQIRMrZnFWbGDFmKoe0b8SNJ6iYnFR8SgQi5WjzjmwuHTmBurWqq5icVBr6LxUpJ+7O78dM\nYeGaLTwypCfN91IxOakclAhEysmzXy7krSkruOH4Azh0v8ZxhyMSmc4aEikjd2fUd0v481szOe7A\n5lz6s33jDkmkRJQIRMpg0dotjBgzla/mr+XQfRvzt7O660djUunEkgjM7ATgIaAa8E93vyeOOERK\nKyfXeeZEE+1jAAAKG0lEQVSLBdz//mxq7LEHfznjYAYf0kZJQCqllCcCM6sGPAb8HFgKfGdm49x9\nRqpjESmN2Ss3ceOYKUxesp7jDmzGXacdrKuMSaUWR4ugLzDP3ecDmNko4FSg3BPBIx/NZdzk5eW9\nWklzC9duoX7tGjw8pCendGuhVoBUenEkglbAkoTHS4F++Rcys0uASwDatm1bqg01rV+Ljs3rleq5\nIoU5vGMTrjh6fxrXqxV3KCLlosIOFrv7U8BTAH369PHSrGNw37YM7lu6JCIiki7i+B3BMiCxKHvr\ncJqIiMQgjkTwHdDRzDqYWU1gMDAuhjhERIQYuobcPdvMrgDeIzh99Gl3n57qOEREJBDLGIG7vw28\nHce2RURkd6o1JCKS5pQIRETSnBKBiEiaUyIQEUlz5l6q32qllJllAYtK+fQmwJpyDKe8KK6SUVwl\no7hKpqLGBWWLrZ27Ny1uoUqRCMrCzMa7e5+448hPcZWM4ioZxVUyFTUuSE1s6hoSEUlzSgQiImku\nHRLBU3EHUAjFVTKKq2QUV8lU1LggBbFV+TECEREpWjq0CEREpAhKBCIiaa5KJAIz+5WZTTezXDPr\nk2/eTWY2z8xmm9nxhTx/bzP7wMzmhn8bJSHG0WY2KbwtNLNJhSy30MymhsuNL+84CtjeHWa2LCG2\nkwpZ7oRwH84zsxEpiOs+M5tlZlPM7FUza1jIcinZX8W9fgs8HM6fYma9khVLwjbbmNl/zGxG+P9/\nVQHLHGVmGxLe39uSHVe43SLfl5j2V+eE/TDJzDaa2dX5lknJ/jKzp81stZlNS5gW6TiUlM+iu1f6\nG3Ag0Bn4BOiTML0LMBmoBXQAvgeqFfD8e4ER4f0RwF+THO/fgNsKmbcQaJLCfXcHcH0xy1QL992+\nQM1wn3ZJcly/AKqH9/9a2HuSiv0V5fUDJwHvAAb0B75JwXvXAugV3q8PzCkgrqOAN1P1/xT1fYlj\nfxXwnq4k+MFVyvcXcCTQC5iWMK3Y41CyPotVokXg7jPdfXYBs04FRrn7DndfAMwD+hay3HPh/eeA\n05ITafBNCDgLeDFZ20iCvsA8d5/v7juBUQT7LGnc/X13zw4ffk1wJbu4RHn9pwL/9sDXQEMza5HM\noNx9hbtnhvc3ATMJrgleGaR8f+VzLPC9u5e2YkGZuPtnwA/5Jkc5DiXls1glEkERWgFLEh4vpeAP\nSnN3XxHeXwk0T2JMRwCr3H1uIfMd+NDMJpjZJUmMI9Hvwub504U0R6Pux2S5kODbY0FSsb+ivP5Y\n95GZtQd6At8UMHtA+P6+Y2ZdUxRSce9L3P9Tgyn8y1gc+wuiHYeSst8q7MXr8zOzD4F9Cph1s7u/\nXl7bcXc3s1KdUxsxxiEU3Ro43N2XmVkz4AMzmxV+eyi1ouICngD+RPDB/RNBt9WFZdleecSVt7/M\n7GYgG8goZDXlvr8qGzOrB4wBrnb3jflmZwJt3X1zOP7zGtAxBWFV2PfFgkvkDgJuKmB2XPtrN2U5\nDpVGpUkE7n5cKZ62DGiT8Lh1OC2/VWbWwt1XhM3T1cmI0cyqA2cAvYtYx7Lw72oze5WgKVimD1DU\nfWdm/wDeLGBW1P1YrnGZ2TBgIHCshx2kBayj3PdXAaK8/qTso+KYWQ2CJJDh7mPzz09MDO7+tpk9\nbmZN3D2pBdYivC+x7K/QiUCmu6/KPyOu/RWKchxKyn6r6l1D44DBZlbLzDoQZPZvC1nu/PD++UC5\ntTDyOQ6Y5e5LC5ppZnXNrH7efYIB02kFLVte8vXLnl7I9r4DOppZh/Db1GCCfZbMuE4AbgQGufvW\nQpZJ1f6K8vrHAb8Oz4bpD2xIaOYnRTje9C9gprs/UMgy+4TLYWZ9CT7za5McV5T3JeX7K0GhrfI4\n9leCKMeh5HwWkz06noobwQFsKbADWAW8lzDvZoJR9tnAiQnT/0l4hhHQGPgImAt8COydpDifBS7N\nN60l8HZ4f1+CswAmA9MJukiSve+eB6YCU8J/qBb54wofn0RwVsr3KYprHkFf6KTw9mSc+6ug1w9c\nmvd+Epz98lg4fyoJZ68lMabDCbr0piTsp5PyxXVFuG8mEwy6D0hBXAW+L3Hvr3C7dQkO7A0SpqV8\nfxEkohXArvDYdVFhx6FUfBZVYkJEJM1V9a4hEREphhKBiEiaUyIQEUlzSgQiImlOiUBEJM0pEUiF\nY2anmZmb2QERlh1mZi0THv/TzLoU85wvw7/tzeycskf8v/W+GJYmuCbf9DvMbGv4K9u8aZvLa7si\nZaVEIBXREODz8G9xhhGcZw2Au//G3WcU9QR3HxDebQ+USyIws32AQ9y9m7v/vYBF1gDXlce28m23\n0lQHkIpLiUAqlLBuzuEEP7AZnG/e7y2ocT/ZzO4xszOBPkCGBbXj9zSzT8ysj5ldamb3JTx3mJk9\nGt7P+zZ+D3BE+NxrzOwzM+uR8JzPzax7vhhqm9kzYRwTzezocNb7QKtwXUcU8NKeBs42s70LeM1D\nzezb8Ln/Z2bV8sWJmZ1pZs+G9581syfN7BvgXgvq2L8Wtka+NrNu4XJ3WFBI8BMzm29mVxb7Bkha\nUiKQiuZU4F13nwOsNbPeAGZ2Yjivn7t3B+5191eA8cC57t7D3bclrGcMwS/O85xNULI30Qjgv+Fz\n/05QrmFYuL1OQG13n5zvOZcT1AQ7mKDF8pyZ1SYoYvZ9uK7/FvC6NhMkg90uHmNmB4axHebuPYAc\n4NyidxEQ1JgZ4O7XAn8EJrp7N+APwL8TljsAOJ6g1s/tYW0ikd0oEUhFM4QfD9ij+LF76DjgGQ/r\nDrl7/lruu3H3LGC+mfU3s8YEB8Qvitn2y8DA8GB5IUFJkPwOB0aG25gFLAI6FbPePA8D5+fV4Qkd\nS1CE8DsLrlp3LEGJhuK87O45CTE9H8b0MdDYzPYK573lwfU41hAUMUtmiXWppNS/KBVG2G1yDHCw\nBSV4qwFuZjeUcpWjCC4CNAt41Yupp+LuW83sA4KWx1kUUSW2NNx9vZm9QNCqyGPAc+5eUEnkxHhr\n55u3JeJmdyTcz0GfeSmAWgRSkZwJPO/u7dy9vbu3ARYQXMznA+ACM6sD/0saAJsILtNYkFcJDuqJ\nrYxEBT33nwTf3L9z93UFPOe/hF03YfdRW4KChlE9APyWHw/IHwFn5p1RFPb3twvnrTKzA81sD3bv\n5ioqpqOANf7T6xKIFEqJQCqSIQQH70RjgCHu/i5BddTxYRfK9eH8Z4En8waLE58YHshnElyXtqDy\n41OAnHDw+ZrwOROAjcAzhcT4OLCHmU0FRgPD3H1HIcv+RNhF8yrBdbQJz3C6BXjfzKYQJLy80uAj\nCK4P8SVBpcrC3AH0Dp9/Dz+WMhaJRNVHRRKEv0n4BDjA3XNjDkckJdQiEAmZ2a8Jrvl7s5KApBO1\nCERE0pxaBCIiaU6JQEQkzSkRiIikOSUCEZE0p0QgIpLm/h/iwI6VO2C11wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15cb405cfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting ReLU Activation function\n",
    "h = np.linspace(-10,10,50)\n",
    "out = tf.nn.relu(h)\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    y = sess.run(out)\n",
    "    \n",
    "plt.xlabel('Activity of Neuron')\n",
    "plt.ylabel('Output of Neuron')\n",
    "plt.title('ReLU Activation Function')\n",
    "plt.plot(h, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15e3a8669e8>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/0JEcNXZwvsPpdK0lko8CX4yImsaCiNgC/C3JWw3NzAy496WVrNtay5dP73ln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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15cb4283f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Softmax Activation function\n",
    "h = np.linspace(-5,5,50)\n",
    "out = tf.nn.softmax(h)\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    y = sess.run(out)\n",
    "    \n",
    "plt.xlabel('Activity of Neuron')\n",
    "plt.ylabel('Output of Neuron')\n",
    "plt.title('Softmax Activation Function')\n",
    "plt.plot(h, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:tensorflow]",
   "language": "python",
   "name": "conda-env-tensorflow-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
